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Abstract

Background

Human time perception is influenced by various factors such as attention and drowsiness.
Nevertheless, the impact of cerebral vigilance fluctuations on temporal perception
has not been sufficiently explored. We assumed that the state of vigilance ascertained
by electroencephalography (EEG) during the perception of a given auditory rhythm would
influence its reproduction. Thus, we hypothesised that the re-tapping interval length
and the accuracy of reproduction performance would vary depending on the state of
vigilance determined by EEG.

Methods

12 female and 9 male subjects ranging from 21 to 38 years (M = 25.52, SD = 3.75) participated
in a test paradigm comprising a) a resting EEG for the determination of vigilance
while an auditory rhythm was presented, b) a short activity of the proband to be sure
of sufficient alertness, and c) a tapping task to reproduce the presented rhythm.
Vigilance states of three consecutive 1-sec-EEG-segments of the resting EEG before
the reproduction phase were classified using the Vigilance Algorithm Leipzig (VIGALL).

Conclusion

These findings support the hypothesis of a varying time perception and of speed alterations
of the internal clock after different states of EEG-vigilance, which were automatically
classified by VIGALL. Thus, alterations of cognitive processing may be assessable
by specific EEG-patterns.

Keywords:

Introduction

The human perception of the rate time passes in is rather instable as can be demonstrated
in a number of settings. Alertness and excitement can considerably influence individual
time perception. For instance, while awaiting an intensely anticipated event, time
is perceived to pass rather slowly. Also, time is perceived to decelerate in tedious
situations such as afternoon school lessons. The goal of this study is to verify the
validity of these subjective observations by investigating the influence of objectively
classified states of wakefulness on the subjective sense of time.

The investigation of alterations in time perception has been a subject of research
for decades. There is extensive literature on the topic of how and where in the brain
time is processed [1]. Furthermore, several time modulating factors, such as personality, attention and
emotions have been described in the literature [2]. A separate branch of research focuses primarily on time processing. However so far,
previous studies have not yielded an entirely accepted model [3-7].

Several studies examining patients suffering from neurological disorders such as brain
lesions [8,9], stroke [10] and Parkinson’s disease [11,12] provide evidence that specific brain areas are linked to time processing. Moreover,
data from neuroimaging studies support the importance of these brain areas for time
perception and processing [13-16].

In order to capture of the subjective passing of time, two main standard tasks have
been established: Time production tasks ask subjects to push a button if they think
that a certain time span has already passed. Time estimation tasks request subjects
to evaluate the length of previous time spans by timing seconds or minutes.

Prior publications suggest that individual time perception differs between individuals
(inter-individual variability) and also within a single person (intra-individual variability),
depending on certain conditions or individual states [17,18]. Particularly, the influence of fluctuations of diurnal individual arousal [19,20] is discussed in the literature concerning time perception variations. Thus, a conceivable
approach to investigate individual alterations of time perception might be to determine
individual states of wakefulness. However, the literature provides overlapping wakefulness
concepts and terms: e.g. alertness, arousal and vigilance [21]. The vigilance concept we refer to in our study describes unspecific activation states
of the central nervous system on the sleep-wake spectrum as they are empirically assessable
by EEG. Loomis et al. [22] classified different activation states of the brain on a continuum reaching from
the concentrated awake state to the state of deep sleep on the basis of specific EEG-patterns.
These EEG-vigilance stages have been subdivided (A1, A2, A3, B1, B2/3) by Bente [23] and Roth [24] in dependence on the frequency and topographic distribution of EEG-waves. For detailed
description of the EEG-vigilance stages, see Additional file 1: Table S1. By determining the individual state of EEG-vigilance and measuring the
performance on standard timing tasks, conclusions on the influence of vigilance on
time perception could be drawn.

Overall, the main purpose of the present study was to examine the influence of the
EEG-based vigilance level on time perception using the VIGALL. In our study, we adhere
to the classic internal clock model [5]. We expect that a high level of vigilance leads to a slowing down of the subjective
passing of time. Thus, the internal clock might produce more time units (analogous
to the ticks of a clock) in shorter time intervals. By contrast, we assume that fewer
time units are produced by the internal clock in low vigilance states implicating
an acceleration of the subjective passing of time.

For assessing the individual sense of time, we applied a tapping paradigm according
to findings of previous studies, which provide evidence that the tapping speed correlates
with the speed of the internal clock [4]. Thus, we assume that the re-tapping speed increases significantly after low EEG-vigilance
levels, as external stimuli are subjectively perceived to be accelerated (Additional
file 2: Table S2). Vice versa, we expect a decrease of the re-tapping speed after high EEG-vigilance
states. We additionally hypothesize that the vigilance level has a crucial impact
on the accuracy the perceived rhythm is reproduced with.

Material and methods

Participants

In total, 21 subjects ranging from 21 to 38 years (M = 25.52, SD = 3.75) participated
in the study. The sample consisted of 12 female and 9 male participants not differing
significantly in age (p = 0.884). Volunteers were recruited through advertisement
and received 15€ remuneration. All participants were free of any psychiatric, neurological
or other serious medical condition. Physical health was screened in a semi-structured
interview; mental health was examined according to criteria of the Diagnostic and
Statistical Manual of Mental Disorders (DSM-IV) by applying a German version of the
Structured Clinical Interview for DSM-IV disorders (SKID) [28]. To exclude drug and alcohol abusing subjects, general alcohol and drug consumption
was quantified by exerting the Alcohol Use Disorders Identification Test (AUDIT) [29] and the Drug Use Disorders Identification Test (DUDIT) [30]. The local ethics committee approved the study. Written informed consent was obtained
from each volunteer prior to investigation according to the declaration of Helsinki.

Measures and procedures

To avoid circadian effects, EEGs were recorded at 7 am in a dimmed and sound attenuated
room. To assure vigilance decline, all subjects worked the night before. EEG recording
took place directly after work had finished. Resting EEG recordings were conducted
with closed eyes in a half-lying position during the tapping task. The testing procedure
took about 60 minutes. The VIGALL algorithm was used to classify EEG-vigilance.

Tapping paradigm

EEG was performed with closed eyes in a horizontal position. The tapping paradigm
used in this study contained several trials consisting of a presentation phase, a
short activation of the subject (by an acoustic signal) and a reproduction phase.
During the presentation phase, an auditory rhythm was presented, which had to be re-tapped
in the reproduction phase. Subjects were activated between presentation and reproduction
by an acoustic signal. EEG data were recorded during the presentation phase.

To encourage a decrease in vigilance, subjects were not given any task during the
presentation of the auditory rhythm. The auditory stimulus was consistent in pitch
(350 Hz) and was presented for 200 ms with a constant inter-stimulus-interval of 800
ms. Nevertheless, subjects were informed by the instructor that the rhythm of the
tones alters within a small range to assure that the subjects did not keep solely
the first tone presentation in mind for the reproduction phase.

The auditory rhythm was presented in variable intervals of 30 seconds to 5 minutes.
The goal was to gather maximal data variability due to statistical reasons, i.e. each
participant should ideally reproduce the rhythm after low vigilance phases as frequent
as after high vigilance phases. Therefore, the investigator monitored EEG recording
and state of vigilance in order to be able to actively vary the interval length of
the presentation phase by initiating an acoustic signal. This preselection concerned
the habitual differences of vigilance declines between subjects due to different EEG-vigilance
regulation patterns [31].

The acoustic signal (600 Hz, 200 ms) challenged the subjects to re-tap the recently
perceived sequence of the auditory stimuli on a touchpad for about 15 seconds. The
participants were instructed to push a button quickly in case of when they perceived
the acoustic signal to ensure compliance. To divide vigilance between presentation
and tapping phase, subjects were further briefed to engage in stretching as a short
activity after presentation of an acoustic signal to enhance alertness. Mean inter-tapping-time
of each trial was calculated.

The vigilance stages of the last three consecutive 1-sec-EEG-segments of the resting
EEG before the reproduction phase were classified using the Vigilance Algorithm Leipzig
(VIGALL).

EEG

EEG recordings lasted about one hour without preparation time. EEG was performed by
placing 31 electrodes (sintered silver/silver chloride) according to the extended
10–20 international system. Impedances were kept below 10 kOhm and common average
was used for reference. Data was recorded with 1 kHz sampling rate. To control for
cardial and ocular artefacts, both electrocardiogram (ECG) and electrooculogram (EOG)
were recorded simultaneously. One EOG-electrode was taped on the right forehead and
a reference electrode was fixed on the cheek below the right eye. ECG-electrodes were
set on the right and left wrist. EEG recordings were carried out with the BrainVision
Analyzer 2.0 software (BrainProducts, Gilching, Germany). These were amplified by
a 40-channel-QuickAmp unit (BrainProducts, Gilching, Germany).

EEG data was pre-processed with the Analyzer software package. First, low-pass (70
Hz), high-pass (0.5 Hz) and notch filters (50 Hz, range 5 Hz) were used to filter
raw data EEG sets. Then, correction of EEG-channels with continuous muscle activity
and removal of eye artefacts was performed by using an independent component analysis
(ICA)-based approach [32,33]. Afterwards, the data sets were segmented into consecutive one-second intervals and
again screened for remaining muscle, movement, eye and sweating artefacts. Segments
containing artefacts were excluded from EEG-vigilance stage analysis. To obtain the
frequency band envelope magnitude in μV2 in order to approximate the power of the underlying signal [34], complex demodulation of the EEG-frequency bands 2-4 Hz (delta), 4-8 Hz (theta),
8-12 Hz (alpha) and 12-25 Hz (beta) was computed for all EEG channels. Thereafter,
intracortical averaged squared current densities of frequency band power was calculated
in four predefined regions of interests (ROIs) by using the sLORETA module of the
Vision Analyzer software:

▪Occipital ROI: occipital lobe and the cuneus, as alpha activity during rest is most
prominent in those areas [35].

▪Parietal ROI: superior and inferior parietal lobe, in these areas shifts of alpha
power have been found during the transition phase from full wakefulness to sleep [36,37].

▪Temporal ROI: inferior temporal lobe, most prominent EEG-alpha power has been found
in the inferior lobe during light sleep stages [38].

▪Frontal ROI: anterior cingulate gyrus (ACC) and the medial frontal gyrus, most prominent
EEG alpha power and EEG theta power is located within these areas during drowsiness
[39,40].

According to EEG-source estimates in the ROIs, EEG-vigilance stages were classified
by the VIGALL algorithm (see Additional file 1: Table S1). Lower vigilance stages, characterised by K-complexes and sleep spindles,
did not occur within data sets. For statistical analysis, vigilance stages of the
last three consecutive 1-sec-segments of the presentation phase were evaluated. EEG-vigilance
sub-stages were subsumed under main EEG-vigilance stages A (A1, A2, A3) and B (B1,
B2/3).

Data preparation

Several EEG recordings had to be terminated prematurely due to an increased proportion
of apparent artefacts as a consequence of decreased relaxation of the participant
or similar reasons. The number of executed trials during testing procedure varied
due to EEG quality and EEG-vigilance regulation patterns [31]. Subjects with rare vigilance switches (stable vigilance regulation patterns) were
tested for a longer period to obtain a broad vigilance variance. On average, the procedure
contained 25 trials (SD = 7.52) per subject. VIGALL classified the EEG-vigilance levels
for the last three seconds of the presentation phase. In case of a stable vigilance
state within these three seconds, i.e. three equal VIGALL-vigilance classifications
within the three-second-sequence, trials were included for calculating the average
vigilance-specific re-tapping interval length per subject. Hereby, we focussed on
the last three seconds, as we supposed that the actual vigilance has an influence
on the participants’ perception. Besides, trials with tapping standard deviations
greater than 1000 ms were treated as missing values as they potentially reflect overt
omissions and errors due to e.g. key mal-functions. Trials with response times above
2000 ms were excluded from further analysis for the same reason. Additionally, subjects
fulfilled a further inclusion criterion if at least two re-tapping trials per EEG-vigilance
stage A and B were available. Thus, three subjects had to be excluded from statistical
analyses. 18 (10 female and 8 male) subjects ranging from 21 to 38 years (MW = 25.78,
SD = 3.96) remained for statistical comparison between the main EEG-vigilance stages
A and B. Detailed analyses between separate sub-stages were not computed because most
subjects did not reach each EEG-vigilance sub-stage.

For analysis of re-tapping accuracy, we calculated the absolute value of deviation
of the re-tapped rhythm from the given rhythm for EEG-vigilance stage A and B.

Statistical analyses

All data were processed using the PASW Statistics 18.0 Package for Windows. Tapping
performance data from trials were averaged for each EEG-vigilance main stage (A and
B). The mean length of the inter-tapping-interval and the mean absolute value of deviation
from the presented rhythm was calculated for EEG-vigilance stage A and B for each
subject. Participants performed at least two trials (MA = 11.89, SDA = 5.290, MB = 6.78, SDB = 5.298) during each vigilance stage. Paired t-tests were used to assess the vigilance
effect on tapping speed and accuracy. Hypotheses were tested one-tailed, the α-level
was set to p = 0.05, marginal trends were determined up to significance level 0.10.

Results

Vigilance

The relative proportion of the main vigilance stages A and B was determined for each
participant. A-stages (M = 64.81%, SD = 21.01%) occurred significantly more frequently
(t(17) = 3.283, p < 0.05) than B-stages (M = 35.19%, SD = 21.01%). There were no significant
sex differences in the frequency of occurrence of the different stages.

Tapping speed and vigilance

Mean inter-tapping-intervals were calculated for each participant concerning the two
main EEG-vigilance conditions (stage A and B). Paired t-tests were used to assess
the difference of the tapping speed within the vigilance conditions. When comparing
the inter-tapping-intervals of A- (M = 0.806 s, SD = 0.067 s), and B-stages (M = 0.823
s, SD = 0.087 s), the tapping speed (Figure 1) was significantly reduced (t(17) = −2.190, p < 0.05) after a lower level of EEG-vigilance
(stage B). The mean inter-tapping-intervals of the A- and B-stages did not differ
significantly from the given interval of 800 ms (tA(17) = 0.396, p = 0.697; tB(17) = 1.137, p = 0.271). No sex differences were observed.

Tapping accuracy and vigilance

Subjects’ mean deviations from the given rhythm were calculated for A- and B-stages
(Figure 2). Vigilance has a significant impact on the accuracy of reproducing rhythms (t(17)
= −2.733, p < 0.05) as subjects reproduced rhythms more precisely after higher vigilance
stage A (MW = 0.046, SD = 0.049) than after lower vigilance stage B (MW = 0.065, SD
= 0.067). There were no significant sex differences in the tapping accuracy in dependence
of the vigilance stage.

Figure 2 .Mean value of the absolute deviation from the given interval length of 800 ms ± SD
(N = 18). Subjects re-tapped the given rhythm more accurately in case of high EEG-vigilance
stage A.

Discussion

The aim of our study was to assess the impact of EEG-vigilance on time perception.
We revealed that the level of wakefulness significantly influences the temporal perception
of external events. However, the direction of the tapping speed effect is contrary
to our initial hypothesis. While we assumed that the tapping speed might be enhanced
after lower vigilance stage B due to the slowing down of the internal clock and perception
of more external events per time unit, subjects tapped faster succeeding high vigilance
stage A. According to our accuracy hypothesis, subjects re-tapped the auditory rhythm
more accurately after high vigilance stage A than after low vigilance stage B.

A possible explanation for this result which is contrary to our initial hypothesis
might be that the difference in vigilance between presentation phase and tapping phase
was too small. Subjects who were in a drowsy state during both experimental phases
might have tapped slower due to their lower activation level. The acoustic signal
and the short activity were possibly not strong enough stimuli to trigger a sustained
central nervous activation. Moreover, subjects who were in the high vigilance stage
A during the presentation and reproduction phase might have tapped faster owing to
an accelerated internal clock. Furthermore, the omitted variability of the auditory
rhythm might have affected the subjects’ compliance.

Assuming that the vigilance stage during the presentation of the auditory rhythm is
similar to the vigilance stage during the tapping phase, our results are consistent
with the outcome of several studies examining the impact of arousal on time perception.
A study by Droit-Volet [2] demonstrated that the presentation length of emotional pictures is more likely to
be overestimated compared to neutral stimuli due to an increased level of arousal
in case of perceiving emotional pictures. Gil et al. [41] reported an acceleration of the internal clock in the case of an enhanced anger-associated
arousal. Furthermore, findings of an early study by Anliker [42] concerning the relationship between variations in alpha voltage of the electroencephalogram
and time perception are in accordance with our results. By determining the percentage
of alpha voltage, states of consciousness were categorized as “very drowsy”, “relaxed”,
or “alert”. In contrast to our study, where vigilance stages were determined by the
automatic EEG-algorithm VIGALL, vigilance states were classified by simple identification
of alpha voltage in this study. More drowsy states caused extended tapping phases,
because subjects underestimated the passage of time and thus overproduced time lengths.
Werboff [43] reported that basic EEG patterns, which were recorded before the experiment started,
predict the tendency to overestimate or underestimate short temporal intervals during
the experimental phase. Subjects showing less than 50 per cent alpha waves (low vigilance
group) in the closed-eyes condition were compared to subjects showing more than 50
per cent alpha waves (high vigilance group). Subjects of the latter group significantly
overestimated time intervals in comparison to the former group. These results provide
support for our hypothesis that states of vigilance detected by EEG directly influence
time judgement.

Another explanation for the results of our study concerning the re-tapping speed might
also be associated with altering internal clock speed. Although previous time estimation
studies are based on the theory that the internal clock accelerates in more alert
states and decelerates in drowsy states, this conclusion can not be drawn in every
experimental paradigm. Due to a lacking observability of the speed of the internal
clock, it can not be assumed with reasonable certainty that it indeed decelerates
during drowsy states and accelerates during alert states.

In depression, several studies have reported an overestimation of length of time passing
in depressed patients [44-48], whereas other studies have observed an underestimation of time [49] or did not find a definite alteration of time perception [50-52]. In contrast to several studies carried out with depressed patients, manic patients
have only been investigated in three studies [44,49,53]. All three studies found an overestimation of time in time estimation tasks. During
manic episodes, time perception might therefore be similar to that found in ADHD [54,55].

Both ADHD and mania are characterized by an unstable wakefulness regulation assessed
by EEG measures of vigilance, ratings of sleepiness and deficits in sustained attention
tasks. It has been postulated that in both mania and ADHD, this unstable wakefulness
regulation represents a central pathogenetic factor leading to attention deficits
and inducing hyperactive, impulsive and sensation-seeking behaviour as an autoregulatory
attempt to stabilize wakefulness by increasing external stimulation [56].

Although our results clearly show significant correlations between vigilance states
and time perception, several limitations should be pointed out. With 18 healthy subjects,
the sample size was relatively small. Thus, it remains uncertain whether the findings
are generalizable. This drawback becomes more evident for the analysis of vigilance
sub-stages (A1, A2, A3, B1 and B2/3). In applying only a time production task, the
obtained findings are not applicable to other time judgement paradigms, such as time
estimation tasks. Investigations with large cohorts of patients and healthy controls
with different standard tasks are required to validate the observed influence of the
vigilance state on time perception. Another shortcoming of our study refers to the
influence of emotion and personality on time perception [2,41]. As we did not control for individual differences, an interaction of present emotional
arousal or stable personality and time perception can not be ruled out. Furthermore,
we did not control for a regular sleep-wake schedule. Thus, the subjects might differ
in their initial state of wakefulness, compliance and motivation in dependence on
their sleep habits.

In general, our study indicates that EEG-based vigilance stages are associated with
cognitive function, as e.g. time perception. Consequently, alterations of cognitive
processing may be assessable by specific EEG-patterns and certain processing prototypes
of EEG-vigilance stages. Therefore, the classification of EEG-vigilance stages via
VIGALL may assist in identifying clinically relevant variations. Our findings regarding
time perception during unstable wakefulness might also help to substantiate hypotheses
regarding the role of wakefulness regulation in the pathophysiology of manic episodes.
Nevertheless, further studies are needed to assess the general connection between
the VIGALL-classified vigilance status and cognitive functioning.

Competing interests

All authors declare not to have any conflict of interest including any financial,
personal or other relationships with other people or organizations that could inappropriately
influence, or be perceived to influence, their work. However, Prof. Himmerich received
speaker honoraria from AstraZeneca, Lilly, Bristol-Myers Squibb and Servier, consulting
fees from Bristol-Myers Squibb, and chemical substances for study support from AstraZeneca,
Novartis and Wyeth. Prof. Hegerl received in the last three years honoraria as speaker
or advisor from Lilly, Wyeth, Lundbeck, Bristol-Myers Squibb, Takeda and Sanofi-Aventis
as well as a consultant for Nycomed.

Authors' contributions

The presented work was carried out in collaboration between all authors. All authors
were involved in drafting and revising critically the manuscript and approved the
final version for publication. MT and UH defined the research theme and planned the
conception of the study. MT and CS designed the experiment methods and acquired data.
JM also acquired data. JM and HH made substantial contributions to the conception
and design, analyzed the data, interpreted the results and wrote the paper. SO and
AS directed the EEG recording methods and discussed analyses, interpretation and presentation.
All authors read and approved the final manuscript.

Disclosure statement

This work was supported by the Federal Ministry of Education and Research (BMBF),
Germany, FKZ: 01EO1001.

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